%0 Journal Article
%T Design of N-11-Azaartemisinins Potentially Active against <i>Plasmodium falciparum</i> by Combined Molecular Electrostatic Potential, Ligand-Receptor Interaction and Models Built with Supervised Machine Learning Methods
%A Jeferson Stiver Oliveira de Castro
%A Jos¨¦ Cir¨ªaco Pinheiro
%A S¨ªlvia Simone dos Santos de Morais
%A Heriberto Rodrigues Bitencourt
%A Antonio Flor¨ºncio de Figueiredo
%A Marcos Antonio Barros dos Santos
%A F¨¢bio dos Santos Gil
%A Ana Cec¨ªlia Barbosa Pinheiro
%J Journal of Biophysical Chemistry
%P 1-29
%@ 2153-0378
%D 2023
%I Scientific Research Publishing
%R 10.4236/jbpc.2023.141001
%X N-11-azaartemisinins potentially active against Plasmodium falciparum are designed by combining molecular electrostatic potential (MEP), ligand-receptor interaction, and models built with supervised machine learning methods (PCA, HCA, KNN, SIMCA, and SDA). The optimization of molecular structures was performed using the B3LYP/6-31G* approach. MEP maps and ligand-receptor interactions were used to investigate key structural features required for biological activities and likely interactions between N-11-azaartemisinins and heme, respectively. The supervised machine learning methods allowed the separation of the investigated compounds into two classes: cha and cla, with the properties ¦ÅLUMO+1 (one level above lowest unoccupied molecular orbital energy), d(C6-C5) (distance between C6 and C5 atoms in ligands), and TSA (total surface area) responsible for the classification. The insights extracted from the investigation developed and the chemical intuition enabled the design of sixteen new N-11-azaartemisinins (prediction set), moreover, models built with supervised machine learning methods were applied to this prediction set. The result of this application showed twelve new promising N-11-azaartemisinins for synthesis and biological evaluation.
%K Antimalarial Design
%K MEP
%K Ligand-Receptor Interaction
%K Supervised Machine Learning Methods
%K Models Built with Supervised Machine Learning Methods
%U http://www.scirp.org/journal/PaperInformation.aspx?PaperID=123424